Product Data Scientist (Growth)
OpenArt AI · Full-time · Posted 5d ago
OpenArt is an AI Storytelling and Visual Creation Platform used by millions worldwide. We’re building the next generation of creative tools powered by cutting-edge AI, enabling anyone to create videos, visuals, characters, and stories with unprecedented speed and imagination. We believe the future of creativity is AI-native, and we’re shaping that future.
Ride an inflection point. OpenArt has grown 7–10X in revenue over the past 2 years in one of the hottest categories in tech. Every experiment you run compounds against a steep trajectory.
Own the experimentation practice — you'll be the person who defines how OpenArt tests, learns, and ships product changes with real statistical rigor.
Direct impact on the funnel. Your work will shape activation, conversion, monetization, and retention for millions of creators.
Meaningful equity, real upside at an early-stage company with strong momentum.
Cross-functional partnership with Product, Design, Engineering, and Data team.
Build from 0 → 1. Experimentation infrastructure, growth measurement, and retention frameworks are being laid down now — you'll shape them.
High ownership, low process, fast iteration environment.
We're looking for a Product Data Scientist focused on growth and retention to be the analytical partner PMs and engineers rely on when they ship changes to OpenArt's product experience.
This role sits at the intersection of experimentation, product analytics, and growth strategy. Your primary job is to turn data into product decisions that move the funnel — activation, conversion, monetization, and retention — through rigorous A/B testing and sharp behavioral insights.
You'll work closely with Product, Engineering, and Growth — designing and analyzing experiments across signup, onboarding, activation, paywall/pricing, feature adoption, and retention loops. You'll also do the deep-dive analyses that shape what teams decide to build in the first place: funnel diagnostics, cohort behavior, LTV segmentation, and retention driver analysis.
Design, run, and analyze A/B tests and other causal experiments across signup, onboarding, activation, monetization, and retention surfaces.
Set the bar for experimental rigor at OpenArt — help decide test sizing, MDE, run duration, primary/secondary metrics, and readout standards.
Debug and resolve tricky test issues — sample ratio mismatch, novelty effects, interaction effects, multiple comparisons, segment heterogeneity.
Build and evolve OpenArt's experimentation practice as we scale — playbooks, guardrails, and post-launch validation.
Deep-dive analyses on the growth funnel: signup → activation → paid conversion → retention → resurrection.
Own retention analysis — identify what drives long-term retention vs. churn, and translate that into product recommendations.
LTV and cohort analyses that shape pricing, packaging, and monetization decisions.
Behavioral segmentation — find the user segments that matter and what drives their behavior.
Opportunity sizing — quantify the potential impact of proposed product changes before engineering builds them.
Define and own key product metrics (activation, engagement, retention, monetization) alongside PMs.
Build and validate dashboards that leaders trust.
Ensure metric definitions stay consistent across product, marketing, and finance.
Translate analyses into crisp, opinionated recommendations that PMs, engineers, and execs act on.
Push back with data when a proposed experiment is underpowered, mis-scoped, or measuring the wrong thing.
3–6 years of experience in data science or analytics supporting product, growth, or lifecycle teams (level and title flex based on candidate).
Solid A/B testing depth. You've designed, run, and analyzed a lot of experiments.
Strong SQL and Python — comfortable working with data end-to-end without hand-holding.
Solid statistical foundation
Product intuition and insight-generation instinct
Excellent communication — you can tell a clear data story to a PM, an engineer, or a CPO, and hold your own in a room with strong opinions.
High ownership and comfort in ambiguity.
Experience as an early hire at a consumer startup.
Experience with consumer products, marketplaces, or subscription businesses.
Experience with experimentation platforms (in-house or vendor: Statsig, Optimizely, etc.).
Familiarity with causal inference methods beyond A/B tests (diff-in-diff, synthetic control, propensity matching, CUPED).
Experience with our stack: BigQuery, Amplitude, Metabase, Stripe, GCP.
Exposure to AI/ML products or generative AI.
Comfort working across time zones with a globally distributed team.
BigQuery, SQL, Python, Amplitude, Metabase, Stripe, GCP
Competitive base salary and bonus program
Equity — meaningful ownership in what you build
High autonomy, high growth environment
Bay Area preferred (hybrid allowed)
Visa sponsorship available
We’ll consider remote for exceptional candidates
Germany
United States
India